One advantage of an authoritarian leadership, however, is an ability to make dramatic decisions quickly. In the summer of 1986, Fanuc formed another joint venture, creating GE-Fanuc Automation with the US. firm General Electric to market factory automation equipment other than robots. In keeping with Fanuc's global strategy of international alliances and a division of labor, Fanuc would supply its NC controllers and other hardware while GE would provide engineering know-how, communications software, and computer technology. In the early 1980s, GE had been a contender for world leadership in factory automation and robotics. But six months after signing its agreement with Fanuc, in January 1987, GE announced that it would abandon the robot side of its business. For Fanuc, this meant the elimination of another competing robot manufacturer and the transformation of it into one more software-oriented ally.

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We have to have a consensus." At a huge Ricoh plant in Atsugi, where paper copier machines and other devices are manufactured by people and robots, an introductory video carefully emphasizes that "technology is for human beings, not the reverse," and a brochure illustrates factory automation with a pyramid—of software and hardware on the bottom, and humans on top. Even Dr. Inaba of Fanuc, the Genghis Khan of robots and automation, keeps in mind the limits of technology. In his 1982 biography, he stresses that "factory automation is not to be used to completely un-man the factory. It is a system to reduce labor, and shift people from monotonous to more creative work. It should only be used when this principle is clearly understood." When construction of a new motor plant with 101 robots produced a surplus of fifty engineers, "we had them do work," Inaba writes, "where they could apply their talents.

The industrial internet will automate certain repetitive jobs that have so far resisted automation because they require some degree of human judgment and spatial understanding — driving a truck, perhaps, or recognizing a marred paint job on an assembly line.
In fast-growing fields like health care, displaced workers might be absorbed into other low- or medium-skill roles, but in others, the economic tradeoffs will be similar to those in factory automation: higher productivity, lower prices for consumers, continued feasibility of manufacturing in high-cost countries like the United States — but also fewer jobs for people without high-demand technical skills.
Everything becomes a sensor
Any machine that registers state data can become a valuable sensor when it’s connected to a network, regardless of whether it’s built for the express purpose of logging data.

Much of the reason for this is that manufacturing, the big employer of the twentieth century (and the path to the middle class for entire generations), is no longer creating net new jobs in the West. Although factory output is still rising in such countries as the United States and Germany, factory jobs as a percentage of the overall workforce are at all-time lows. This is due partly to automation, and partly to global competition driving out smaller factories.
Automation is here to stay—it’s the only way large-scale manufacturing can work in rich countries (see chapter 9). But what can change is the role of the smaller companies. Just as startups are the driver of innovation in the technology world, and the underground is the driver of new culture, so, too, can the energy and creativity of entrepreneurs and individual innovators reinvent manufacturing, and create jobs along the way.

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Wages in the industrial provinces such as Guangdong are rising at 17 percent per year, and the creeping revaluing of the yuan only makes that worse in real terms. American workers are also up to three times more productive (not because they’re necessarily more skilled or harder-working, but because they tend to be matched with more automation, which amplifies individual productivity). The Boston Consulting Group estimates that the net cost of manufacturing in China will be the same as that in the United States by 2015.39
And as factory automation becomes more powerful, the labor component of the average product drops. And that means that the traditional labor arbitrage arguments for moving manufacturing jobs overseas will diminish. Right now, in the automotive industry, labor represents less than 15 percent of the cost of the vehicle (the United Auto Workers union claims that it is just 10 percent, but that includes only assembly-line workers, not office, management, and R&D).

For all three sets of winners and losers, the news is troubling. Let’s look at each in turn.
1. High-Skilled vs. Low-Skilled Workers
We’ll start with skill-biased technical change, which is perhaps the most carefully studied of the three phenomena. This is technical change that increases the relative demand for high-skill labor while reducing or eliminating the demand for low-skill labor. A lot of factory automation falls into this category, as routine drudgery is turned over to machines while more complex programming, management, and marketing decisions remain the purview of humans.
A recent paper by economists Daron Acemoglu and David Autor highlights the growing divergence in earnings between the most-educated and least-educated workers. Over the past 40 years, weekly wages for those with a high school degree have fallen and wages for those with a high school degree and some college have stagnated.

Each time the task changes—each time the location of the screw holes move, for example—production must stop until the machinery is reprogrammed. Today’s factories, especially large ones in high-wage countries, are highly automated, but they’re not full of general-purpose robots. They’re full of dedicated, specialized machinery that’s expensive to buy, configure, and reconfigure.
Rethinking Factory Automation
Rodney Brooks, who co-founded iRobot, noticed something else about modern, highly automated factory floors: people are scarce, but they’re not absent. And a lot of the work they do is repetitive and mindless. On a line that fills up jelly jars, for example, machines squirt a precise amount of jelly into each jar, screw on the top, and stick on the label, but a person places the empty jars on the conveyor belt to start the process.

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With technology as a multiplier, an economy is able to produce more output each year with the same inputs, including labor. And in the basic model all labor is affected equally by technology, meaning every hour worked produces more value than it used to.
A slightly more complex model allows for the possibility that technology may not affect all inputs equally, but rather may be ‘biased’ toward some and against others. In particular, in recent years, technologies like payroll processing software, factory automation, computer-controlled machines, automated inventory control, and word processing have been deployed for routine work, substituting for workers in clerical tasks, on the factory floor, and doing rote information processing.
By contrast, technologies like big data and analytics, high-speed communications, and rapid prototyping have augmented the contributions made by more abstract and data-driven reasoning, and in turn have increased the value of people with the right engineering, creative, or design skills.

That results in an astonishing ability to rapidly ramp up production or adjust to product design changes, but it also puts extreme pressure on workers—as evidenced by the near epidemic of suicides that occurred at Foxconn facilities in 2010. Robots, of course, have the ability to work continuously, and as they become more flexible and easier to train for new tasks, they will become an increasingly attractive alternative to human workers, even when wages are low.
The trend toward increased factory automation in developing countries is by no means limited to China. Clothing and shoe production, for example, continues to be one of the most labor-intensive sectors of manufacturing, and factories have been transitioning from China to even lower-wage countries like Vietnam and Indonesia. In June 2013, athletic-shoe manufacturer Nike announced that rising wages in Indonesia had negatively impacted its quarterly financial numbers.

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The technology will be used where it is most cost-effective: for example, in creating those parts that need to be customized, or perhaps in printing complex components that would otherwise require extensive assembly. Where 3D printing can’t be used to directly fabricate high-volume parts, it will often find a role in rapidly creating the molds and tools required in traditional manufacturing techniques. In other words, 3D printing is likely to end up being another form of factory automation. Manufacturing robots and industrial printers will work in unison—and increasingly without the involvement of workers.
Three-dimensional printers can be used with virtually any type of material, and the technology is finding many important uses outside of manufacturing. Perhaps the most exotic application is in printing human organs. San Diego–based Organovo, a company that specializes in bio-printing, has already fabricated experimental human liver and bone tissue by 3D-printing material containing human cells.

What happens when machines become workers—when capital becomes labor?
Copyrighted Material – Paperback/Kindle available @ Amazon
THE LIGHTS IN THE TUNNEL / 98
It is important to note that such a change in the relationship between workers and machines will have a worldwide impact. Advanced machine automation will come to
low wage countries as well as developed nations. A 2003
article in AutomationWorld pointed out that “productivity
gains spawned by factory automation are driving a worldwide decline in manufacturing jobs, even in developing
nations.”33 According to the article, even back in 2003,
automation was causing significant job loss in Brazil, India
and China.
We cannot succumb to the temptation to assume that
the rising middle classes in China and India are going to
solve the demand problem. Our simulation in Chapter 1
used just one tunnel to represent the entire world mass
market.

Because sensors seem to be watching and listening to you, as well as understanding what you are doing, they, like big data, sometimes freak people out.
Sensors go back a very long way. In the mid-1600s Evangelista Torricelli, an Italian physicist, invented a way to measure atmospheric pressure by using mercury in a vacuum tube called a Torricellian Tube. Most people know it as a barometer.
Sensors’ full capability began about 50 years ago when factory automation started to come into play. Unlike people, sensors work tirelessly, never needing sleep and never demanding a raise. They notice changes where humans miss them, thus ensuring labels are correctly affixed to bottles moving through a factory assembly line. They are used in nuclear power plants for early detection of leaks.
Some semiconductor foundries, such as TSMC in Taiwan, are attempting to build what’s known as “lights-out factories,” where sensors will eliminate the need for any employees at all.

The system is then able to apply the reasoning from its stored cases to new situations.
Robots are extensively used in manufacturing. The latest generation of robots uses flexible Al-based machine-vision systems—from companies such as Cognex Corporation in Natick, Massachusetts—that can respond flexibly to varying conditions. This reduces the need for precise setup for the robot to operate correctly. Brian Carlisle, CEO of Adept Technologies, a Livermore, California, factory-automation company, points out that "even if labor costs were eliminated [as a consideration], a strong case can still be made for automating with robots and other flexible automation. In addition to quality and throughput, users gain by enabling rapid product changeover and evolution that can't be matched with hard tooling."
One of AI's leading roboticists, Hans Moravec, has founded a company called Seegrid to apply his machine-vision technology to applications in manufacturing, materials handling, and military missions.203 Moravec's software enables a device (a robot or just a material-handling cart) to walk or roll through an unstructured environment and in a single pass build a reliable "voxel" (three-dimensional pixel) map of the environment.

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In my current projects teams of just three or four people achieve in a few months objectives that are comparable to what twenty-five years ago required a team of a dozen or more people working for a year or more.
Software Complexity. Twenty years ago software programs typically consisted of thousands to tens of thousands of lines. Today, mainstream programs (for example, supply-channel control, factory automation, reservation systems, biochemical simulation) are measured in millions of lines or more. Software for major defense systems such as the Joint Strike Fighter contains tens of millions of lines.
Software to control software is itself rapidly increasing in complexity. IBM is pioneering the concept of autonomic computing, in which routine information-technology support functions will be automated.7 These systems will be programmed with models of their own behavior and will be capable, according to IBM, of being "self-configuring, self-healing, self-optimizing, and self-protecting."

And so the tiger chases its tail.
There is ample evidence to suggest that these plants are highly repre- sentative among manufacturing organizations in the manner in which they have chosen to interpret new information technology. One Hon- eywell Corporation survey, which probed human resource planning in- stituted by major corporations in conjunction with factory automation, found that only one company out of fifteen had a recognized method for assessing the human resource impact of factory automation. Not a single firm had a process for actually addressing impacts, from educa- tional needs to work force reduction issues. 5 Another survey of plant managers conducted by Honeywell Corporation among its major cus- tomers for integrated information and control technology found that, almost without exception, the "technology ideal" reported by plant managers was having one screen in their office from which they could operate the entire plant. 6 Ramchandran Jaikumar studied installations of flexible manufacturing systems and concluded that u.s. managers tend to use these systems in ways that rigidify the production process and increase central control, thus missing the real opportunities for adaptibility and customization such systems can provide. 7 In a detailed study of the history of numerical control machine tools, David Noble documents the series of technological choices that fa- vored forms of automation which concentrated knowledge and control in the managerial
omain.

Highly specific visual thinkers should skip algebra and study more visual forms of math such as trigonometry or geometry. Children who are visual thinkers will often be good at drawing, other arts, and building things with building toys such as Legos. Many children who are visual thinkers like maps, flags, and photographs. Visual thinkers are well suited to jobs in drafting, graphic design, training animals, auto mechanics, jewelry making, construction, and factory automation.
2. Music and math thinkers think in patterns. These people often excel at math, chess, and computer programming. Some of these individuals have explained to me that they see patterns and relationships between patterns and numbers instead of photographic images. As children they may play music by ear and be interested in music. Music and math minds often have careers in computer programming, chemistry, statistics, engineering, music, and physics.

During the 1880s and 90s, new technologies are developed in response to economic and social crises, coming together at the start of the third cycle.
1890s–1945: In the third cycle heavy industry, electrical engineering, the telephone, scientific management and mass production are the key technologies. The break occurs at the end of the First World War; the 1930s Depression, followed by the destruction of capital during the Second World War terminate the downswing.
Late-1940s–2008: In the fourth long cycle transistors, synthetic materials, mass consumer goods, factory automation, nuclear power and automatic calculation create the paradigm – producing the longest economic boom in history. The peak could not be clearer: the oil shock of October 1973, after which a long period of instability takes place, but no major depression.
In the late–1990s, overlapping with the end of the previous wave, the basic elements of the fifth long cycle appear. It is driven by network technology, mobile communications, a truly global marketplace and information goods.

Here is the real crisis in innovation under the Impulse Society. Where innovation was once a tool to improve the productivity of the entire economy—companies and workers, capital and labor—it’s more exclusive today. Increasingly, innovation improves the productivity of capital, via faster returns, while leaving labor’s productivity largely unchanged, or even slowed. For instance, where early moves toward factory automation were generally associated with increased worker productivity—that is, each factory worker could now produce more output per hour and thus merit a higher wage—the “innovation” of offshoring has often yielded lower worker productivity. Chinese factory workers in the 1990s were substantially less productive than their U.S. counterparts,15 which companies compensated for by piling on more workers.

The problem is that, contrary to Marx, the introduction and diffusion of the more automated technology did not undermine the position of the adult-male mule spinners in the production process.17 Well into the twentieth century, adult-male mule spinners, known as ‘minders’, remained the principal workers on the ‘self-acting’ machines, and indeed by the final decades of the nineteenth century had become one of the best-organised and best-financed craft unions in Britain.18 More generally, even in the presence of factory automation, skilled shop-floor workers remained central to British manufacturing into the second half of the twentieth century.19
An understanding of where Marx went wrong is of substantial relevance for understanding the sources of productivity growth in the capitalist economy, not only in his time but also in ours. The mechanisation of certain motions on mule spinning machines that led them to be described as ‘self-acting’ still left a number of other functions that required the constant attention of experienced workers.

In 2013, the Federal Aviation Administration reported that an overreliance on automation has become a major factor in air disasters and urged airlines to give pilots more opportunities to fly manually. The best way to make flying even safer than it already is, the agency’s research suggests, may be to transfer some responsibility away from computers and back to people. Where humans and machines work in concert, more automation is not always better.
That’s a lesson that the Toyota Motor Company, a leader in factory automation, has learned the hard way. In recent years, the carmaker has had to recall millions of vehicles to fix defects, putting a dent in its profits and tarnishing its prized reputation for quality. It now believes that its manufacturing problems stem from a loss of human insight and talent. “We need to become more solid and get back to basics,” a company executive told Bloomberg News, “to sharpen our manual skills and further develop them.”

It was all about wa and nemawashi—the uniquely Japanese spirit of cooperation and consultation that Toyota had cultivated with its employees. We were sure that American workers would never put up with these paternalistic practices. Then, of course, Toyota started building plants in the United States, and they got the same results here they got in Japan—so our cultural excuse went out the window. For the next five years, we focused on Toyota’s manufacturing processes. We studied their use of factory automation, their supplier relationships, just-in-time systems, everything. But despite all our benchmarking, we could never seem to get the same results in our own factories. It’s only in the last five years that we’ve finally admitted to ourselves that Toyota’s success is based on a wholly different set of principles—about the capabilities of its employees and the responsibilities of its leaders.
Do You Work at a Place that Ignites Your Passion?

Technical colleges offer a variety of programs and credentials, including AA degrees, certificate programs, and tailored “contract programs” that they design and implement for companies upon request. At Piedmont Technical College’s Center for Advanced Manufacturing in Laurens, South Carolina, fifteen young men (no women) are working in small groups, building model conveyor belts using a combination of sensors (inductive and optical) and a software program. Most of them are in a two-year AA program, working toward a degree in mechatronics, which one of them describes as “the study of factory automation.”
Some of the students in the class came to Piedmont Technical College (PTC) right out of high school, while others took a less direct route: They held low-wage fast-food jobs or low-skill factory jobs. PTC’s promotional materials emphasize that its graduates will be “career ready” the day they graduate and will earn 30 percent more than high-school graduates (they also note that some graduates start out earning more than $50,000 a year).

This is a critical point to keep in mind before we criticize or praise anyone for their predictions, and before we make our own. Every disruptive new technology, any resulting change in the dynamics of society, will produce a range of positive and negative effects and side effects that shift over time, often suddenly. Consider the most discussed impact of the machine age, employment. The avalanche of factory automation, business machines, and domestic labor-saving devices that, starting in the 1950s, led to the disappearance of millions of jobs and entire professions, while skyrocketing productivity created unprecedented economic growth—and the creation of more jobs than had been lost.
Should we pity all the steel-driving John Henrys put out of work by steam engines? Or the office pool typists, assembly-line workers, and elevator operators who had to retool and retrain as technology replaced them by the thousands?

On Up-Front Testing
Being able to fully test the real behavior of individual components in the laboratory can make a 10x or 100x difference to the cost of your project. That confirmation bias engineers have to their own work makes up-front testing incredibly profitable, and late-stage testing incredibly expensive.
I’ll tell you a short story about a project we worked on in the late 1990s. We provided the software, and other teams the hardware, for a factory automation project. Three or four teams brought their experts on-site, which was a remote factory (funny how the polluting factories are always in a remote border country).
One of these teams, a firm specializing in industrial automation, built ticket machines: kiosks, and software to run on them. Nothing unusual: swipe a badge, choose an option, receive a ticket. They assembled two of these kiosks on-site, each week bringing some more bits and pieces.

It was a system that required discipline, and Bradski was a bit of a “Wild Duck”—a term that IBM originally used to describe employees who refused to fly in formation—compared to typical engineers in Intel’s regimented semiconductor manufacturing culture.
A refugee from the high-flying finance world of “quants” on the East Coast, Bradski arrived at Intel in 1996 and was forced to spend a year doing boring grunt work, like developing an image-processing software library for factory automation applications. After paying his dues, he was moved to the chipmaker’s research laboratory and started researching interesting projects. Bradski had grown up in Palo Alto before leaving to study physics and artificial intelligence at Berkeley and Boston University. He returned because he had been bitten by the Silicon Valley entrepreneurial bug.
For a while he wrote academic research papers about machine vision, but he soon learned that there was no direct payoff.

Each robot replaces two to five workers at Earthbound, according to John Dulchinos, an engineer who is the chief executive at Adept Technology, a robot maker based in Pleasanton, Calif., that developed Earthbound’s system.
Robot manufacturers in the United States say that in many applications, robots are already more cost-effective than humans.
At an automation trade show last year in Chicago, Ron Potter, the director of robotics technology at an Atlanta consulting firm called Factory Automation Systems, offered attendees a spreadsheet to calculate how quickly robots would pay for themselves.
In one example, a robotic manufacturing system initially cost $250,000 and replaced two machine operators, each earning $50,000 a year. Over the fifteen-year life of the system, the machines yielded $3.5 million in labor and productivity savings.
The Obama administration says this technological shift presents a historic opportunity for the nation to stay competitive.